
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312360
MD-D-23-09882
00061
10.1097/MD.0000000000039680
3
5200
Research Article
Observational Study
Influence of autonomic nervous dysfunction on eating during hemodialysis sessions: An observational study
Xiao Dan MD 283590336@qq.com
a
Li Xia MD lee3082@sina.com
a
Li Yi MD lee3082@sina.com
a
Mo Liwen MD mlwen2000@163.com
a
Li Xianglian MD lee3082@sina.com
a
Fu Yonggang MD 954081162@qq.com
a
Zhang Fan MD billtd@126.com
a
Wang Tao MD cdjqzyywt@126.com
a
Cheng Yue MD ab*
Li Yunming PhD lee3082@sina.com
cd
Zhou Pengfei MD kkdanx@163.com
c
a Department of Nephrology, General Hospital of Western Theater Command, Chengdu, PR China
b College of Medicine, Southwest Jiaotong University, Chengdu, PR China
c Department of Information, Statistical Office, General Hospital of Western Theater Command, Chengdu, PR China
d Department of Statistics, College of Mathematics, Southwest Jiaotong University, Chengdu, PR China.
* Correspondence: Yue Cheng, Department of Nephrology, General Hospital of Western Theater Command, Chengdu 610083, PR China (e-mail: chengyue20022@hotmail.com).
20 9 2024
20 9 2024
103 38 e3968027 11 2023
17 8 2024
23 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Although some studies have indicated that eating during hemodialysis may induce hypotension and cardiovascular events, some patients still consume food during their treatment. This prospective study was conducted to determine whether the need to eat during hemodialysis treatment was related to abnormal glucose metabolism and autonomic nerve dysfunction. Seventy patients were enrolled in this study, and their demographic features and various laboratory parameters were analyzed. At each routine hemodialysis visit, predialysis, intradialysis, and postdialysis blood pressure measurements were systematically conducted. A 24-hour ambulatory electrocardiogram (ECG) was performed during the hemodialysis interval, and heart rate variability (HRV) values were calculated. Additionally, whether the patients ate during the hemodialysis treatments was recorded. Another 20 people who underwent physical examinations during the same period and were matched for sex and age were included in the control group. The HRV values of the hemodialysis patients were generally lower than those of the control group. Univariate analysis revealed significant differences in sex, age, calcium antagonist use, blood calcium levels, insulin levels, diastolic blood pressure (DBP) measurements, and HRV indices between hemodialysis patients who ate and those who did not eat during hemodialysis (P < .05), whereas there were no significant differences in diabetes status or in the hemoglobin, albumin, blood glucose and C-peptide levels (P > .05). Multivariate analysis revealed that low values for very low frequency (VLF) and postdialysis DBP were risk factors for fasting intolerance during hemodialysis treatments. Autonomic dysfunction may affect whether hemodialysis patients tolerate fasting during dialysis. VLF evaluation may provide information that can be used to develop a more reasonable intradialytic nutritional supplementation method.

autonomic nerve function
fasting
hemodialysis
heart rate variability
OPEN-ACCESSTRUE
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pmc 1. Introduction

Whether hemodialysis patients should be allowed to eat during dialysis remains a controversial topic.[1] The administration of intradialytic meals is a simple and effective approach for enhancing caloric intake and improving nutritional status among patients on hemodialysis. However, recent studies have shown that postprandial redistribution in intravascular volume and increased blood supply to the gastrointestinal circulation may interfere with the adequacy of the delivered hemodialysis and provoke a postprandial decline in blood pressure (BP).[2] These risks have important clinical implications that are not counteracted by the anticipated benefits of intradialytic meals on caloric intake and nutritional status.[3] Therefore, eating during hemodialysis should be done with caution. However, some patients rely on food intake during hemodialysis. For these patients, if they do not eat during dialysis, intradialysis sweating and palpitations occur more frequently and can even result in early termination of the dialysis treatment.

What is the underlying factor that affects patients’ tolerance to fasting during dialysis? Appetite sensations (hunger, satiety, fullness, and the desire to eat) and food intake behavior are regulated by the gut–brain axis (gut–brain communication).[4] The autonomic nervous system is crucial for bidirectional communication between the gut and the brain. Ingestion of a meal triggers numerous sensory signals in the gastrointestinal tract.[5] These signals are then transmitted to the brain, where they contribute to food intake regulation by modulating appetite as well as exerting feedback control of gastrointestinal functions. Some studies have suggested that autonomic dysfunction may contribute to reduced food intake and gastric motility, increased psychological disorders, heightened pain sensation, and induced nausea and vomiting.[6] Understanding the alteration in autonomic function and its pathophysiological role in food intake may provide information for whether hemodialysis patients tolerate fasting during dialysis.

In addition, dialysis patients are at high risk of developing glucose metabolism disturbances, such as diabetes mellitus (DM), impaired fasting glucose (IFG), and impaired glucose tolerance (IGT). Glucose metabolism disturbances are often accompanied by any alterations in appetite sensations (hunger, satiety, fullness, and desire to eat).

Therefore, the following study was conducted to determine whether fasting intolerance during hemodialysis is related to autonomic nervous dysfunction, diabetes, abnormal glucose metabolism, and other conditions.

2. Patients and methods

2.1. Patients

Patients aged 18 to 75 years with end-stage renal disease (ESRD) who were undergoing maintenance hemodialysis for at least 3 months in the Department of Nephrology in the General Hospital of Western Theater Command were included in this study. The exclusion criteria included cardiovascular and cerebrovascular accidents in the past 3 months, hemodialysis combined with abdominal dialysis, a history of gastrointestinal surgery, psychiatric disorders such as anxiety and depression, and heart disease with evident arrhythmias, such as atrial fibrillation. Between June 2021 and December 2021, 70 patients were enrolled in this study. A prospective study was performed on this group of hemodialysis patients. Baseline demographic indices and the use of antihypertensive drugs were recorded at the time of entry into the study. The patients were followed for 3 months. Another 20 people who underwent physical examinations during the same period and were matched for sex and age were included as a control group.

All of the patients received 4 hours of conventional hemodialysis with a bicarbonate bath 3 times weekly (Fresenius, FX60, Germany). The blood flow rates ranged from 200 to 300 ml/min, with a fixed dialysate flow rate of 500 ml/min. The dialysate composition was as follows: Na + 135 to 145 mmol/L, K + 2.0 mmol/L, and Ca++ 1.5 mmol/L. Low-molecular-weight heparin was routinely used during dialysis. Antihypertensive drugs (angiotensin-converting enzyme inhibitors, calcium channel blockers, β blockers, etc) were prescribed when hypertension was still present after the water upload had been well controlled. Dialysis-related variables, including the ultrafiltration rate and equilibrated Kt/V, were collected. The patients’ eating conditions during hemodialysis were recorded; fasting intolerance referred to eating during each dialysis treatment or in more than 2/3rd of dialysis treatments even after instruction regarding dietary practices.

The study protocol was approved by the local ethics committee, and all of the patients provided written informed consent.

2.2. Laboratory methods

Blood samples were drawn at enrollment and after 3 months. Blood was collected on an interval (i.e., nondialysis) day. The White blood cell count, platelet count and serum levels of hemoglobin, albumin, creatinine, phosphate, calcium, potassium, sodium, low-density lipoprotein-cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC), glucose, glycosylated hemoglobin, insulin, and C-peptide were measured via automated and standardized methods at a centralized laboratory. All of the above-referenced clinical data are expressed herein as means and standard deviations (SDs).

2.3. Blood pressure measurements

At each routine hemodialysis visit, BP measurements were performed by trained nurses using a mercury sphygmomanometer with an appropriate cuff on the right or left arm of the patient, who was in the supine position. Predialysis, intradialysis (after 2 hours of dialysis), and postdialysis measurements were taken systematically. The average BP was calculated. The frequency of hypotension during dialysis (systolic BP (SBP) decrease ≥ 20 mm Hg during dialysis or mean arterial pressure decrease ≥ 10 mm Hg, accompanied by spasm, headache, vomiting, stuffiness and other related symptoms, or the need to reduce ultrafiltration or fluid replacement and other interventions) was recorded.

2.4. Ambulatory electrocardiogram

Twenty-four-hour ambulatory ECG was performed the day after hemodialysis. Twelve-channel ambulatory ECG (Edan Instruments, Inc., China, no. EDAN SE-2012, sampling frequency: 128 Hz, analogue-to-digital converter (A/D) conversion accuracy: 16 bits) was used to obtain data related to the heart rate variability (HRV) of patients. The main indicators of time-domain measures included the standard deviation of normal sinus beats (SDNN), standard deviation of all sinus beats (SDRR) (ms), mean of the SDs of all the NN intervals (SDNNI), percentage of adjacent NN intervals that differed from each other by more than 50 ms (pNN50), and root mean square of successive differences between normal heartbeats (RMSSD) (rMSSD) (ms). The main indicators of the frequency-domain measures included LF (0.04–0.15 Hz) (ms2), HF (0.15–0.4 Hz) (ms2), very low frequency (VLF) (0.003–0.04 Hz) (Unit: ms2), and LF/HF (%).

The ECG signal was input into the computer through A/D conversion, and the computer software automatically read the data and generated HRV time-domain and frequency-domain analysis data. Through human–machine dialogue, 2 experienced electrocardiographers identified whether there were arrhythmias and disordered interference waves, such as premature atrial contractions, premature ventricular contractions and junctional tachycardia, that would impede the HRV analysis. The corresponding R waves were marked and automatically deleted. The HRV data software analysis process is shown in Figure 1.

Figure 1. Process of heart rate variability (HRV) analysis. The ECG signal was input into the computer through A/D conversion, and the computer software automatically read the data and generated HRV time-domain and frequency-domain analysis data. Through human–machine dialogue, 2 experienced electrocardiographers identified whether there were arrhythmias and disordered interference waves, such as premature atrial contractions, premature ventricular contractions and junctional tachycardia, that would impede the HRV analysis. The corresponding R waves were marked and automatically deleted.

2.5. Statistical analysis

The Kolmogorov–Smirnov test was used to evaluate the normal distribution of measurement variables. The mean ± SD was used for normally distributed measurement variables, the median (P25, P75) was used for non-normally distributed measurement variables, and the frequency and percentage were used for the statistical description of counting variables.

Two independent-samples t tests were used to compare the means of normally distributed measurement variables between the 2 groups, the Mann–Whitney U test was used to compare the medians of nonnormally distributed measurement variables, and the χ2 test or Fisher exact probability method was used to compare the composition of counting variables.

Patients were divided into a fasting-tolerant group and a fasting-intolerant group according to their tolerance of fasting during dialysis. The influencing factors that differed between the 2 groups (P < .05) in the univariate analysis were included in the multivariate logistic regression model. Factors that met the significance criterion (univariate analysis, P < .05) were considered for inclusion in the multivariate logistic regression model. The stepwise regression method was used to screen the independent factors influencing fasting intolerance in dialysis patients. All of the tests were 2-sided, and differences were considered significant at P < .05. All of the statistical analyses were performed via Statistical Product and Service Solutions (SPSS Inc., Chicago), version 18.

3. Results

3.1. Patient characteristics and heart rate variability

The baseline characteristics and HRV indices of the study population are summarized in Table 1. There were no significant differences in sex or age between the hemodialysis patients and the control group. The HRV indices, including the SDNN, SDNNI, RMSSD, pNN50, HF, LF and VLF, in the hemodialysis patients were lower than those in the control group (Table 1, P < .05).

Table 1 Comparison of gender, age, and heart rate variability indexes between hemodialysis patients and the control group.

		Total	Hemodialysis patients	Normal people	χ2/t/U	P value	
Total		90	70 (77.78)	20 (22.22)			
Gender	Male	46 (51.11)	38 (54.29)	8 (40.00)	1.270*	.260	
	Female	44 (48.89)	32 (45.71)	12 (60.00)			
Age (yr)	Mean ± SD	54.20 ± 13.77	53.67 ± 13.36	56.05 ± 15.36	0.679†	.499	
Age group (yr)	<60	57 (63.33)	48 (68.57)	9 (45.00)	3.722*	.054	
	≥60	33 (36.67)	22 (31.43)	11 (55.00)			
SDNN (ms)	Mean ± SD	102.99 ± 34.33	94.86 ± 31.08	131.45 ± 30.28	4.669†	<.001	
SDNNI (ms)	Median (P25,P75)	35 (28,47)	32 (27,42)	46 (40,55)	4.079‡	<.001	
Rmssd (ms)	Median (P25,P75)	18 (13,23)	16 (12,21)	24 (19,34)	3.830‡	<.001	
PNN50 (%)	Median (P25,P75)	1 (1,4)	1 (0,3)	4 (2,12)	3.671‡	<.001	
TNI	Mean ± SD	20.96 ± 7.63	19.94 ± 6.76	24.50 ± 9.47	2.418†	.018	
HF (ms2)	Median (P25,P75)	65 (35,118)	55 (29,99)	119 (68,245)	3.902‡	<.001	
LF (ms2)	Median (P25,P75)	180 (106,350)	158 (96,271)	283 (205,503)	3.280‡	.001	
VLF (ms2)	Mean ± SD	631.37 ± 353.17	539.25 ± 309.89	953.79 ± 307.95	5.283†	<.001	
LF/HF ratio	Mean ± SD	3.23 ± 2.14	3.45 ± 2.30	2.46 ± 1.16	1.859†	.066	
HF = high frequency, LF = low frequency, PNN50 = Edinburgh index (the percentage of adjacent NN intervals that differ from each other by more than 50ms), RMSSD = the root mean square value of the difference between adjacent NN intervals throughout the whole process, SD = standard deviation, SDNN = standard deviation of all normal sinus heart intervals (NN), SDNNI = SDNN index, TNI = triangular index, VLF = very low frequency.

* χ2 test.

† Two independent sample t-test.

‡ Mann–Whitney U test.

3.2. Univariate analysis results of hemodialysis patients

The results revealed that being female, being ≥ 60 years old, using calcium antagonists and having high serum levels of calcium were related to fasting intolerance. There were no significant differences in diabetes status (Table 2), blood glucose levels or C-peptide concentrations between the patients who tolerated fasting and those who did not tolerate fasting, whereas patients who did not tolerate fasting were found to have higher insulin levels than patients who tolerated fasting (Table 3, P = .004). There were no significant differences in body mass index (BMI) or in hemoglobin and albumin levels between patients who tolerated fasting and those who did not tolerate fasting (Tables 2 and 3). The HRV indices of the hemodialysis patients who did not tolerate fasting were generally lower (i.e., almost all HRV evaluation indices) than those of patients who tolerated fasting (Table 4, P < .05).

Table 2 Demographics and clinical characteristics of the study population.

		Total	Tolerance to fasting	Intolerance to fasting	χ2	P value	
Total		70 (100.00)	33 (47.14)	37 (52.86)			
Gender	Male	38 (54.29)	23 (60.53)	15 (39.47)	5.975*	.015	
	Female	32 (45.71)	10 (31.25)	22 (68.75)			
Smoke	Never	64 (91.43)	31 (48.44)	33 (51.56)		.677†	
	Ever	6 (8.57)	2 (33.33)	4 (66.67)			
Diabetes mellitus	No	60 (85.71)	31 (51.67)	29 (48.33)	3.449*	.063	
	Yes	10 (14.29)	2 (20.00)	8 (80.00)			
Cardiovascular and cerebrovascular events	No	57 (81.43)	30 (52.63)	27 (47.37)	3.711*	.054	
	Yes	13 (18.57)	3 (23.08)	10 (76.92)			
Beta blocker	no	45 (64.29)	21 (46.67)	24 (53.33)	0.011*	.915	
	yes	25 (35.71)	12 (48.00)	13 (52.00)			
RAS blocker	no	39 (55.71)	21 (53.85)	18 (46.15)	1.588*	.208	
	yes	31 (44.29)	12 (38.71)	19 (61.29)			
Calcium antagonist	no	16 (22.86)	12 (75.00)	4 (25.00)	6.459*	.011	
	yes	54 (77.14)	21 (38.89)	33 (61.11)			
Age group (yr)	<60	48 (68.57)	27 (56.25)	21 (43.75)	5.083*	.024	
	≥60	22 (31.43)	6 (27.27)	16 (72.73)			
Duration of dialysis group (yr)	<57.3	44 (62.86)	20 (45.45)	24 (54.55)	0.136*	.713	
	≥57.3	26 (37.14)	13 (50.00)	13 (50.00)			
Kt/V group	<1.33	47 (67.14)	26 (55.32)	21 (44.68)	3.838*	.050	
	≥1.33	23 (32.86)	7 (30.43)	16 (69.57)			
Ultrafiltration ratio group (%)	<3	12 (17.14)	7 (58.33)	5 (41.67)	1.451*	.484	
	3-	39 (55.71)	19 (48.72)	20 (51.28)			
	≥5	19 (27.14)	7 (36.84)	12 (63.16)			
BMI group	<18.5	12 (17.14)	5 (41.67)	7 (58.33)	0.189*	.910	
	18.5-	41 (58.57)	20 (48.78)	21 (51.22)			
	≥24	17 (24.29)	8 (47.06)	9 (52.94)			
BMI = body mass index, RAS = renin–angiotensin system.

* χ2 test.

† Fisher exact probability method.

Table 3 The laboratory indexes of the study population.

		Total	Tolerance to fasting	Intolerance to fasting	χ2	P value	
Total		70 (100.00)	33 (47.14)	37 (52.86)			
Hemoglobin group (g/L)	<90	6 (8.57)	2 (33.33)	4 (66.67)		.911†	
	90-	24 (34.29)	11 (45.83)	13 (54.17)			
	105-	26 (37.14)	13 (50.00)	13 (50.00)			
	≥120	14 (20.00)	7 (50.00)	7 (50.00)			
Albumin group (g/L)	<40	8 (11.43)	2 (25.00)	6 (75.00)	1.777*	.182	
	≥40	62 (88.57)	31 (50.00)	31 (50.00)			
Glucose group (mmol/L)	<6.32	53 (75.71)	28 (52.83)	25 (47.17)	2.833*	.092	
	≥6.32	17 (24.29)	5 (29.41)	12 (70.59)			
Triglyceride group (mmol/L)	<1.86	47 (67.14)	26 (55.32)	21 (44.68)	3.838*	0.050	
	≥1.86	23 (32.86)	7 (30.43)	16 (69.57)			
Total cholesterol group (mmol/L)	<4.22	37 (52.86)	19 (51.35)	18 (48.65)	0.558*	.455	
	≥4.22	33 (47.14)	14 (42.42)	19 (57.58)			
Sodium group (mmol/L)	<135	9 (12.86)	5 (55.56)	4 (44.44)	0.293*	.588	
	≥135	61 (87.14)	28 (45.90)	33 (54.10)			
Calcium group (mmol/L)	<2.5	57 (81.43)	31 (54.39)	26 (45.61)		.014†	
	≥2.5	13 (18.57)	2 (15.38)	11 (84.62)			
Phosphorus group (mmol/L)	<1.78	30 (42.86)	12 (40.00)	18 (60.00)	1.075*	.300	
	≥1.78	40 (57.14)	21 (52.50)	19 (47.50)			
Insulin group	<11.84	50 (71.43)	29 (58.00)	21 (42.00)	8.278*	.004	
	≥11.84	20 (28.57)	4 (20.00)	16 (80.00)			
C-peptide group (ng/ml)	<7.83	37 (52.86)	18 (48.65)	19 (51.35)	0.071*	.789	
	≥7.83	33 (47.14)	15 (45.45)	18 (54.55)			
glycosylated hemoglobin group (%)	<6.5	59 (84.29)	30 (50.85)	29 (49.15)	2.068*	.150	
	≥6.5	11 (15.71)	3 (27.27)	8 (72.73)			
* χ2 test.

† Fisher exact probability method.

Table 4 Comparison of blood pressure and heart rate variability indexes between patients who tolerated fasting and those who did not tolerate fasting.

		Total	Tolerance to fasting	Intolerance to fasting	t/U	P value	
Total		70	33 (47.14)	37 (52.86)			
predialysis SBP	mean ± SD	136.43 ± 18.83	139.39 ± 18.99	133.78 ± 18.54	1.250*	.216	
intradialytic SBP	mean ± SD	132.71 ± 17.99	135.00 ± 16.86	130.68 ± 18.94	1.004*	.319	
postdialysis SBP	mean ± SD	131.93 ± 17.53	134.70 ± 18.50	129.46 ± 16.49	1.253*	.215	
predialysis DBP	mean ± SD	79.57 ± 11.48	82.88 ± 10.68	76.62 ± 11.49	2.351*	.022	
intradialytic DBP	mean ± SD	78.43 ± 11.90	82.27 ± 9.85	75.00 ± 12.64	2.662*	.010	
postdialysis DBP	mean ± SD	76.57 ± 11.44	79.85 ± 10.79	73.65 ± 11.34	2.336*	.022	
SDNN	mean ± SD	94.86 ± 31.08	111.58 ± 30.24	79.95 ± 23.53	4.911*	<.001	
SDNNI	median (P25,P75)	32 (27,42)	40 (34,48)	27 (23,31)	5.219†	<.001	
Rmssd	median (P25,P75)	16 (12,21)	18 (15,21)	14 (11,18)	2.647†	.008	
PNN50	median (P25,P75)	1 (0,3)	1 (1,3)	1 (0,2)	1.890†	.059	
HF	median (P25,P75)	55 (29,99)	72 (43,119)	43 (18,72)	2.871†	.004	
LF	median (P25,P75)	158 (96,270)	259 (170,469)	106 (54,158)	5.101†	<.001	
VLF	median (P25,P75)	494 (304,748)	748 (580,962)	308 (223,406)	6.559†	<.001	
LF/HF ratio	median (P25,P75)	2.87 (1.85,5.03)	3.25 (2.58,5.11)	2.02 (1.37,3.78)	3.124†	.002	
DBP = diastolic blood pressure, HF = high frequency, LF = low frequency, PNN50 = Edinburgh index (the percentage of adjacent NN intervals that differ from each other by more than 50ms), RMSSD = The root mean square value of the difference between adjacent NN intervals throughout the whole process, SBP = systolic blood pressure, SDNN = standard deviation of all normal sinus heart intervals (NN), SDNNI = SDNN index, VLF = very low frequency.

* Two independent sample t-test.

† Mann–Whitney U test.

3.3. Multivariate logistic regression analysis

Multivariate logistic regression analysis was performed to assess the independent association of each parameter with fasting intolerance during dialysis. The analysis revealed a significant relationship between fasting intolerance during dialysis and VLF (Table 5, OR = 0.986, P < .001) and between fasting intolerance during dialysis and postdialysis diastolic BP (DBP) (Table 5, OR = 0.877, P = .038). The associations above remained significant after adjustment for age, sex, calcium antagonists and serum levels of calcium.

Table 5 Multivariate logistic regression analysis.

	Crude OR	95% CI	P value	Adjust-OR	95% CI	P value	
VLF	0.987	0.981 to 0.993	<.001	0.986	0.978 to 0.993	<.001	
postdialysis DBP	0.949	0.907 to 0.994	.028	0.877	0.775 to 0.993	.038	
intradialytic DBP	0.944	0.901 to 0.989	.014				
predialysis DBP	0.949	0.906 to 0.994	.027				
Gender (female)	3.373	1.252 to 9.087	.016				
Age group (≥60)	3.429	1.144 to 10.279	.028				
Calcium antagonist (yes)	4.714	1.341 to 16.569	.016				
Triglyceride group (≥1.86)	2.830	0.982 to 8.153	.054				
Calcium group (≥2.5)	6.558	1.332 to 32.294	.021				
Kt/V group (≥1.33)	2.830	0.982 to 8.153	.054				
Insulin group (≥11.84)	5.524	1.613 to 18.921	.007				
SDNN	0.953	0.928 to 0.978	<.001				
SDNNI	0.917	0.868 to 0.969	.002				
RMSSD	0.917	0.925 to 1.019	.229				
HF	0.999	0.997 to 1.001	<.001				
LF	0.991	0.986 to 0.997	.991				
LF/HF ratio	0.787	0.625 to 0.992	.042				
DBP = diastolic blood pressure, HF = high frequency, LF = low frequency, OR = odds ratio, RMSSD = The root mean square value of the difference between adjacent NN intervals throughout the whole process, SDNN = standard deviation of all normal sinus heart intervals (NN), SDNNI = SDNN index, VLF = very low frequency.

4. Discussion

Eating practices during hemodialysis treatment vary across countries, regions, and clinics. Kistler et al[6] surveyed clinicians regarding their clinical practices during the 2014 International Society of Renal Nutrition and Metabolism Conference. The results revealed that 61 clinics (85%) allowed patients to eat during treatment, and 53 clinics (73%) provided food during hemodialysis. A greater proportion of physicians subscribed to the view that the administration of intradialytic meals and supplements is a simple and effective approach to enhancing caloric intake and improving nutritional status among patients on hemodialysis; however, eating during hemodialysis treatment increases the incidence of symptomatic intradialytic hypotension and increases the risk of cardiovascular events.[7,8] Recent studies suggest that these risks are not counteracted by the anticipated benefits of increased caloric intake and improved nutritional status. Some researchers recommend that feeding during hemodialysis should be individualized according to each patient nutritional requirements and risk profile for adverse intradialytic events.

In practice, we have observed that some patients cannot tolerate fasting during hemodialysis treatment. Our study revealed that 37 patients asked for food during every hemodialysis treatment or in more than two-thirds of the hemodialysis treatments. These patients had a significantly greater incidence of hypotension during dialysis (20.00 [8.50, 24.00], median [P25, P75]) than did the 33 patients who tolerated fasting (2.00 [1.00, 5.00], median [P25, P75]) (Z = 6.284, P < .001), which is consistent with previous reports that eating during dialysis increases the incidence of hypotension. The findings raised the following question: What factors affect a patient’s tolerance to fasting during hemodialysis?

Differences between patients who tolerated fasting and those who did not tolerate fasting, especially in terms of autonomic nerve function, diabetes and BP, were studied. The results revealed that there were no significant differences in diabetes status, blood glucose levels or C-peptide concentrations between the patients who tolerated fasting and those who did not tolerate fasting. However, the insulin levels were greater in patients who did not tolerate fasting, indicating that there might be more severe insulin resistance in patients with fasting intolerance. There were no significant differences in BMI or in the hemoglobin and albumin levels between patients who tolerated fasting and those who did not tolerate fasting, indicating that fasting during dialysis did not increase the risk of malnutrition.

Previous studies have indicated that autonomic nerve dysfunction is common in patients with ESRD.[9,10] HRV was used to evaluate autonomic nerve function in this study. HRV is a commonly used index for evaluating autonomic nerve function. The American Electrocardiographic Society recommends the evaluation of HRV, which has been widely used in the quantification and diagnosis of diabetes-related autonomic nerve function since as early as the 1970s. The measurement methods are divided into short-term measurements (5 minutes), ultrashort-term measurements (<5 minutes) and long-term measurements (24 hours). Since longer recording epochs better represent processes with slower fluctuations (e.g., circadian rhythms) and given the cardiovascular system’s response to a wider range of environment stimuli and workloads, a 24-hour ECG was used in this study. The analytical methods mainly involved frequency-domain and time-domain analytical methods. Time-domain analysis is a quantitative analytical method based on the cardiac cycle (R–R interval) difference histogram. The frequency-domain analysis is based on the instantaneous heart rate change trend chart. By decomposing it into a different number of sinusoidal curves with different frequencies, different amplitudes, and different phases, spectral curves are drawn according to the power distributions of the different sinusoidal curves, and then, through the fast Fourier transform method (FFT algorithm) or autoregressive analytical method (AR algorithm), these heart rate change curves are converted into spectra to reveal heart rate activity patterns. In the present study, both time-domain and frequency-domain metrics[11] were used. The results revealed a decrease in HRV in hemodialysis patients, which was consistent with the findings of previous investigations.[12] Moreover, both the time-domain (SDNN, SDNNI, and RMSSD) and frequency-domain (HF, LF, VLF, and LF/HF) HRV values in hemodialysis patients who could not tolerate fasting were significantly lower than those in patients who could tolerate fasting. Further multivariate analysis revealed that VLF was an important factor affecting the inability of hemodialysis patients to tolerate fasting. It is speculated that autonomic nerve dysfunction may be involved in the process of fasting intolerance during hemodialysis.

Previous experimental evidence suggests that the heart intrinsically generates the VLF (0.0033–0.04 Hz) rhythm and that efferent sympathetic nervous system (SNS) activity due to physical activity and stress responses modulates its amplitude and frequency.[13–15] The VLF rhythm is influenced by thermoregulation and the renin–angiotensin system[16,17] and may be fundamental to health.[18] Low VLF power has been shown to be associated with arrhythmic death,[19] high inflammation, and posttraumatic stress disorder.[20,21] Previous studies have shown that VLF power is more strongly associated with all-cause mortality than LF or HF power is.[22] It is speculated that a lower VLF may reflect abnormal autonomic nerve function and dysfunction of the renin–angiotensin system under the stress state of hemodialysis. Therefore, low VLF is not only associated with fasting intolerance but is also an important predictor of cardiovascular prognosis. Patients with fasting intolerance during dialysis may have a greater cardiovascular risk.

In addition, the results of the multivariate analysis suggested that a lower postdialysis DBP was also an important factor affecting patients’ fasting intolerance. It is speculated that the lower postdialysis DBP may be related to insufficient activation of the renin–angiotensin system and insufficient sympathetic excitability after dehydration. However, this study did not reveal a correlation between SBP during dialysis, DBP before and during dialysis and fasting intolerance during hemodialysis. Whether a lower postdialysis DBP indicates fasting intolerance or eating causes a decrease in postdialysis DBP necessitates further interventional cohort studies; such studies could accurately evaluate whether eating during dialysis increases the risk of postdialysis DBP decline in dialysis patients who do not tolerate fasting.

In conclusion, the present investigation revealed that only lower VLF power and lower postdialysis DBP were risk factors for fasting intolerance during hemodialysis; however, the pathophysiological mechanism is complex and needs to be further explored. Considering the findings of previous studies, it is speculated that patients with lower VLF power are more intolerant to fasting; furthermore, such patients may have a greater risk of fatal arrhythmia and sudden cardiac death. Consequently, individualized nutrition plans applied during dialysis are particularly important for patients who are more prone to adverse prognoses.

4.1. Limitations of this study

There are several limitations to this study. First, the sample size was small, and the observation period of 3 months was not long enough to evaluate the occurrence of cardiovascular events. The results of this study suggested a relationship between autonomic dysfunction and fasting intolerance, but the study design may not allow causation to be established. Further interventional cohort studies should be conducted on hemodialysis patients for a sufficient length of time to clarify the causal relationship between autonomic dysfunction and fasting intolerance and to accurately assess whether eating during dialysis increases the risk of cardiovascular events in hemodialysis patients who do not tolerate fasting. Second, in this study, the patients’ intolerance to fasting presented some subjective symptoms, such as sweating and palpitations. Consequently, there may have been bias in the evaluation. HRV was used to evaluate autonomic nerve function in this study. If more indicators, such as the autonomic reflex test (Valsalva maneuver and heart rate changes during deep breathing) and measurements of blood catecholamines and derivative substances, are used to evaluate autonomic nervous system function, the results will be more convincing.

Acknowledgments

This study was funded by the Project of Sichuan Administration of Traditional Chinese Medicine (2021MS513) and the Scientific Research Project of Western Theater General Hospital, China (2021-XZYG-C38).

Author contributions

Data curation: Xia Li, Yunming Li, Pengfei Zhou.

Formal analysis: Yunming Li.

Funding acquisition: Yue Cheng.

Investigation: Xia Li, Yi Li, Yonggang Fu.

Methodology: Dan Xiao, Yue Cheng.

Project administration: Yi Li, Xianglian Li, Yonggang Fu, Yue Cheng.

Resources: Liwen Mo, Fan Zhang.

Software: Liwen Mo, Yunming Li.

Supervision: Xia Li, Yi Li, Xianglian Li, Fan Zhang, Tao Wang, Yue Cheng.

Writing – original draft: Dan Xiao, Yue Cheng.

Writing – review & editing: Yue Cheng.

Abbreviation:

ADBP average diastolic blood pressure

ASBP average systolic blood pressure

BMI body mass index

DBP diastolic blood pressure

ECG electrocardiogram

ESRD end-stage renal disease

HF high frequency

HRV heart rate variability

LDL-C low-density lipoprotein-cholesterol triglyceride

LF low frequency

pNN50 percentage of adjacent NN intervals that differ from each other by more than 50 ms

RAS renin–angiotensin system

RMSSD root mean square of successive differences between normal heartbeats

SDNN standard deviation of normal sinus beats

SDNNI the mean of the standard deviations of all the NN intervals

SNS sympathetic nervous system

TC total cholesterol

TG triglycerides

TNI triangular index

VLF very low frequency

This study complies with the principles of the Helsinki Declaration and was approved by the Ethics Committee of the General Hospital of Western Theater Command.

The authors declare that they have no conflicts of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Xiao D, Li X, Li Y, Mo L, Li X, Fu Y, Zhang F, Wang T, Cheng Y, Li Y, Zhou P. Influence of autonomic nervous dysfunction on eating during hemodialysis sessions: An observational study. Medicine 2024;103:38(e39680).

DX and XL contributed equally to this article.
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